Intrinsic Resistance to Immune Checkpoint Blockade in a Mismatch Repair–Deficient Colorectal Cancer The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Gurjao, Carino et al. "Intrinsic Resistance to Immune Checkpoint Blockade in a Mismatch Repair–Deficient Colorectal Cancer." Cancer immunology research 7 (2019):1230-1236 © 2019 The Author(s) As Published 10.1158/2326-6066.cir-18-0683 Publisher American Association for Cancer Research (AACR) Version Author's final manuscript Citable link https://hdl.handle.net/1721.1/124400 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Cancer Manuscript Author Immunol Res. Author Manuscript Author manuscript; available in PMC 2020 February 01. Published in final edited form as: Cancer Immunol Res. 2019 August ; 7(8): 1230–1236. doi:10.1158/2326-6066.CIR-18-0683. Intrinsic Resistance to Immune Checkpoint Blockade in a Mismatch Repair Deficient Colorectal Cancer Carino Gurjao1,2, David Liu1,2, Matan Hofree2, Saud H. AlDubayan1,2, Isaac Wakiro3, Mei-Ju Su4, Kristen Felt5, Evisa Gjini5, Lauren K. Brais1, Asaf Rotem3, Michael H. Rosenthal6, Orit Rozenblatt-Rosen2, Scott Rodig5,7, Kimmie Ng1, Eliezer M. Van Allen1,2,3, Steven M. Corsello1,2, Shuji Ogino2,8,9,10, Aviv Regev2, Jonathan A. Nowak8,9, Marios Giannakis1,2 1.Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA 2.Broad Institute of MIT and Harvard, Cambridge, MA, USA 3.The Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, MA, USA 4.Biotherapeutic and Medicinal Sciences, Biogen, Cambridge MA, USA 5.Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA 6.Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA 7.Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA 8.Department of Oncologic Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA 9.Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 10.Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA Abstract Immunotherapy with checkpoint inhibitors, such as the programmed death-1 (PD-1) antibodies pembrolizumab and nivolumab, are effective in a variety of tumors, yet not all patients respond. Tumor microsatellite instability-high (MSI-H) has emerged as a biomarker of response to checkpoint blockade, leading to the tissue-agnostic approval of pembrolizumab in MSI-H cancers. Here we describe a patient with MSI-H colorectal cancer that was treated with this immune checkpoint inhibitor and exhibited progression of disease. We examined this intrinsic resistance through genomic, transcriptional, and immunohistochemical characterization of the patient’s tumor and the associated immune microenvironment. The tumor had typical MSI-H molecular features, including a high neoantigen load. We also identifed biallelic loss of the gene for β2- microglobulin (B2M), whose product is critical for antigen presentation. Immune-infiltration deconvolution analysis of bulk transcriptome data from this anti–PD-1–resistant tumor and hundreds of other colorectal cancer specimens revealed a high natural killer (NK) cell and M2 macrophage infiltration in the patient’s cancer. This was confirmed by single-cell transcriptome analysis and multiplex immunofluorescence. Our study provides insight into resistance in MSI-H Gurjao et al. Page 2 tumors and suggests immunotherapeutic strategies in additional genomic contexts of colorectal Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author cancer. Introduction Immune checkpoint inhibitors, such as programmed cell death 1 (PD-1, PDCD1) antibodies, have revolutionized cancer treatment by demonstrating long-lasting responses in patients with several types of malignancies (1). However, only a subset of patients experience benefit from these agents and complete response remains uncommon. In this context, tumor DNA mismatch repair deficiency (dMMR) and a high-level of microsatellite instability (MSI-H) have emerged as powerful genomic markers of response to immune checkpoint inhibitors across malignancies (2, 3), leading to the tissue-agnostic FDA-approval of the PD-1 antibody pembrolizumab in refractory dMMR/MSI-H solid malignancies and to the approval of PD-1 antibody nivolumab with or without the CTLA-4 antibody ipilimumab in dMMR/ MSI-H colorectal cancer (CRC) after fluoropyrimidine, oxaliplatin and irinotecan-based chemotherapy. The leading proposed reason for the immunogenicity of dMMR tumors is their high mutational and neoantigen burden (4); however, only 30–55% of patients with such cancers respond to immune checkpoint blockade with another 10–28% of patients remaining primarily refractory to immunotherapy (2 3, 5, 6). To date, the molecular and microenvironmental features of dMMR/MSI-H tumors that are intrinsically resistant to immune-checkpoint blockade remain unknown. Their characterization could provide insights for novel combination immunotherapies in this subset of tumors and also inform resistance, and strategies to overcome it, in additional genomic contexts. Here, we describe a patient with metastatic dMMR CRC who was treated with pembrolizumab after combination chemotherapy. Despite having confirmed dMMR/MSI-H status and a high neoantigen load, her disease progressed on pembrolizumab. To analyze the basis of this intrinsic immune checkpoint inhibitor resistance, we performed bulk and single- cell characterization of her tumor and the associated immune microenvironment. Materials and Methods Patient study The patient provided written consent to participate in research protocols for additional core biopsies and research testing. All biopsies and molecular testing were performed in accordance with protocols approved by the IRB at the Dana-Farber Cancer Institute. Statistical analyses We used R-3.4.4 to perform the statistical analyses. For two-group comparisons, significance was evaluated by the Mann–Whitney U test for non-normal distributions, and with a two- tailed student t test otherwise. P values of < 0.05 were considered statistically significant. Bulk sequencing DNA and RNA extractions from Formalin Fixed Paraffin Embedded (FFPE) sections and peripheral blood were carried out using standard methods (7). Whole-exome sequencing Cancer Immunol Res. Author manuscript; available in PMC 2020 February 01. Gurjao et al. Page 3 (WES) was performed as detailed previously (8) on the pre-immunotherapy tumor and Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author peripheral blood, with mean depth of coverage of 270× and 101×, respectively. For bulk whole-transcriptome sequencing (RNA-seq), we used the TCap (Transcriptome Capture) protocol (genomics.broadinstitute.org/products/whole-transcriptome-sequencing), which is optimal for low-input and degraded samples such as FFPE samples. Using this method, RNAseq was performed on the pre-treatment tumor with >22.000 genes and 99.4% exons detected. Single-cell sequencing The core biopsy was received in additive free M199 media (Thermofisher Scientific; #11150059). To generate a cell suspension for single-cell RNA-seq (scRNA-seq), the core was minced into smaller ~1-mm pieces, which were then dissociated by a combination of mechanical and enzymatic digestion with Accumax (Innovative Cell Technologies; #AM105) at room temperature for 10 minutes. Following dissociation, cells were strained through a 100 μm strainer, washed with ice cold PBS (Ca/Mg free) with 2% FCS and resuspended in 0.04% BSA (Thermofisher Scientific #AM2616) with PBS. From this suspension, two channels were loaded on 10x; one with 4000 cells and the other with 6000 cells. Libraries were prepared using established protocols. Droplet-based massively parallel scRNA-seq was performed using Chromium Single cell 3’ Reagents Kits (v.1) according to the manufacturers protocols (10x Genomics). The generated scRNA-seq libraries were sequenced using 100 cycle Illumina HiSeq. After quality control, 595 resulting cells were used for further analyses. Variant calling Tumor somatic mutations were called from WES using standardized pipelines including MuTect for somatic SNV inference and Strelka for small insertion/deletions. We corrected for FFPE and oxoguanine artifacts, and used a panel of normal filter as previously described (9). Tumor purity and ploidy were inferred using ABSOLUTE, and cancer cell fraction (CCF) of mutations (i.e. the proportion of tumor cells with the mutation) estimated. Allelic copy number alterations were inferred using an adaptation of a circular binary segmentation (10) and corrected for tumor purity and ploidy. The mutations discussed were orthogonally validated by a next-generation CLIA-certified sequencing panel (11). In order to study the mutational signatures in the tumor of the patient, we used DeconstructSig based on linear combination analysis of preexisting signatures. POLYSOLVER was used to detect the HLA type of the patient, which enabled neoantigen prediction using NetMHCpan as previously described (9). MLH1 methylation testing DNA methylation patterns in the CpG island of the MLH1 promoter gene were determined